Package: stlTDNN 0.1.0

Girish Kumar Jha

stlTDNN: STL Decomposition and TDNN Hybrid Time Series Forecasting

Implementation of hybrid STL decomposition based time delay neural network model for univariate time series forecasting. For method details see Jha G K, Sinha, K (2014). <doi:10.1007/s00521-012-1264-z>, Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.

Authors:Girish Kumar Jha [aut, cre], Ronit Jaiswal [aut, ctb], Kapil Choudhary [ctb], Rajeev Ranjan Kumar [ctb]

stlTDNN_0.1.0.tar.gz
stlTDNN_0.1.0.tar.gz(r-4.5-noble)stlTDNN_0.1.0.tar.gz(r-4.4-noble)
stlTDNN_0.1.0.tgz(r-4.4-emscripten)stlTDNN_0.1.0.tgz(r-4.3-emscripten)
stlTDNN.pdf |stlTDNN.html✨
stlTDNN/json (API)

# Install 'stlTDNN' in R:
install.packages('stlTDNN', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • Data_potato - Normalized Monthly Average Potato Price of India

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1 exports 0.00 score 71 dependencies 152 downloads

Last updated 4 years agofrom:54e5d27291. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 26 2024
R-4.5-linuxOKAug 26 2024

Exports:STLTDNN

Dependencies:askpassclicodetoolscolorspacecurlDerivfansifarverforeachforecastfracdiffgenericsggplot2glmnetgluegreyboxgtablehttrisobanditeratorsjsonlitelabelinglatticelifecyclelmtestmagrittrMAPAMASSMatrixmgcvmimemunsellneuralnetnlmenloptrnnetnnforopensslpillarpkgconfigplotrixpracmaquadprogquantmodR6RColorBrewerRcppRcppArmadilloRcppEigenrlangscalesshapesmoothstatmodsurvivalsystexregtibbletimeDatetseriestsutilsTTRurcaurootutf8vctrsviridisLitewithrxtablextszoo